Segmentation Guided Local Proposal Fusion for Co-Saliency Detection Conference Paper uri icon


  • © 2017 IEEE. We address two issues hindering existing image co-saliency detection methods. First, it has been shown that object boundaries can help improve saliency detection; But segmentation may suffer from significant intra-object variations. Second, aggregating the strength of different saliency proposals via fusion helps saliency detection covering entire object areas; However, the optimal saliency proposal fusion often varies from region to region, and the fusion process may lead to blurred results. Object segmentation and region-wise proposal fusion are complementary to help address the two issues if we can develop a unified approach. Our proposed segmentation-guided locally adaptive proposal fusion is the first of such efforts for image co-saliency detection to the best of our knowledge. Specifically, it leverages both object-aware segmentation evidence and region-wise consensus among saliency proposals via solving a joint co-saliency and co-segmentation energy optimization problem over a graph. Our approach is evaluated on a benchmark dataset and compared to the state-of-the-art methods. Promising results demonstrate its effectiveness and superiority.

author list (cited authors)

  • Tsai, C., Qian, X., & Lin, Y.

citation count

  • 8

publication date

  • July 2017